Geographic Trends and Geospatial Analysis of Head and Neck Fellowship‐Trained Surgeons
Bibliographic record
Abstract
BACKGROUND: Disparities in access to otolaryngology and cancer care exist across the United States. However, little is known about the geographic distribution of fellowship-trained head and neck cancer (HNC) surgeons. METHODS: A cross-sectional study of American Head and Neck Society (AHNS) fellowship graduates from July 1, 1997 to June 30, 2022 was conducted. Geospatial and statistical analysis was conducted to assess current practice location and correlations with training regions. RESULTS: Among 688 graduates, 622 practice in the US or Canada. Most graduates remained in the region of their training. Geospatial analysis showed concentration of graduates in urban areas, with 152 of 3142 US counties having higher-than-expected density. Underserved regions were identified in the southeastern US, southern border, and western states. CONCLUSIONS: Head and neck surgical fellowship graduates predominantly practice in large urban areas, leaving rural and underserved regions with limited access to complex HNC care. Strategic interventions are needed to address these gaps.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".